Genetic evidence of population subdivision among Masai giraffes separated by the Gregory Rift Valley in Tanzania

Abstract The Masai giraffe has experienced a population decline from 70,000 to 35,000 in the past three decades and was declared an endangered subspecies by the IUCN in 2019. The remaining number of Masai giraffe are geographically separated by the steep cliffs of the Gregory Rift escarpments (GRE) in Tanzania and Kenya dividing them into two populations, one west and one east of the GRE. The cliffs of the GRE are formidable barriers to east–west dispersal and gene flow and the few remaining natural corridors through the GRE are occupied by human settlements. To assess the impact of the GRE on Masai giraffe gene flow, we examined whole genome sequences of nuclear and mitochondrial DNA (mtDNA) variation in populations located east (Tarangire ecosystem) and west (Serengeti ecosystem) of the GRE in northern Tanzania. Evidence from mtDNA variation, which measures female‐mediated gene flow, suggests that females have not migrated across the GRE between populations in the Serengeti and Tarangire ecosystems in the past ~289,000 years. The analysis of nuclear DNA variation compared to mtDNA DNA variation suggests that male‐mediated gene flow across the GRE has occurred more recently but stopped a few thousand years ago. Our findings show that Masai giraffes are split into two populations and fulfill the criteria for designation as distinct evolutionary significant units (ESUs), which we denote as western Masai giraffe and eastern Masai giraffe. While establishing giraffe dispersal corridors across the GRE is impractical, conservation efforts should be focused on maintaining connectivity among populations within each of these two populations. The importance of these efforts is heightened by our finding that the inbreeding coefficients are high in some of these Masai giraffe populations, which could result in inbreeding depression in the small and fragmented populations.


| INTRODUC TI ON
As a result of human activities wild mammal populations have declined over the past 10,000 years and now account for <4% of mammal biomass on the planet, with humans, pets, and livestock constituting ~96% ( Bar-On et al., 2018;Ritchie et al., 2022). During the past few decades, the charismatic megaherbivores on the African continent, including giraffes (Giraffa camelopardalis), elephants (Loxodonta cyclotis and L. africana), and rhinoceroses (Diceros bicornis and Ceratotherium simum), have experienced dramatic reductions in population sizes, population fragmentation, and the threat of extinction (IUCN, 2022). The causes of the massive decline in mammals-especially megaherbivores-are numerous and complex but all appear to stem from human activities including conversion of natural habitats to agriculture and human settlements, diverting and depleting water sources, legal and illegal hunting, and humaninduced climate change (Ripple et al., 2015).
Recent population genomic analysis of the major giraffe subspecies suggested that the decline in giraffe population abundance began soon after the separation of distinct subspecies and their dispersal across sub-Saharan Africa during the middle Pleistocene (Coimbra et al., 2021) in parallel with a rapid decline of all ruminants (Chen et al., 2019) and an increase in human populations, hunting, and introduction of zoonotic diseases from livestock. The viral disease rinderpest was introduced to the African continent in the 1890s and caused several mass mortality events for ruminant wildlife including giraffes over a period of 70 years (Plowright, 1982). More recently, the global giraffe population declined 36%-40% (from 1985 to 2015) as a consequence of human activities, with <100,000 individuals remaining (Muller et al., 2018). The Masai giraffe (G. c. tippelskirchi), found in southern Kenya and throughout Tanzania, declined by 50% in three decades to approximately 35,000 individuals and was listed as an endangered subspecies in 2019 (Bolger et al., 2019).
Geographically, the steep cliffs of the Gregory Rift Escarpments (GRE; including the Manyara-Natron, and Eyasi Escarpments) bisect the Masai giraffe populations in northern Tanzania into two distinct regions: west of the GRE including the Serengeti Ecosystem and east of the GRE including the Tarangire Ecosystem ( Figure 1a). In total <14,000 Masai giraffe are reported to exist in these two ecosystems ( Figure 1b) (Bolger et al., 2019). The Serengeti Ecosystem (~33,000 km 2 ) and the Tarangire Ecosystem (~25,000 km 2 ) are two of the most critical ecosystems in Tanzania for biodiversity conservation. Both ecosystems conserve biodiversity and large landscapes, and support two of Africa's few remaining long-distance migrations of large mammals including the white-bearded wildebeests (Connochaetes taurinus) and the plains zebras (Equus quagga) and along with their major predators including lions (Panthera leo) and leopards (Panthera pardus) Estes, 2014;Guy et al., 1981;Hopcraft et al., 2013;Lamprey, 1964;Lohay et al., 2022;Morrison et al., 2016;Prins & de Jong, 2022;Sinclair, 2012). Each ecosystem also host two of the largest remaining populations of Masai giraffes (Figure 1b), (Bolger et al., 2019;Lee & Bolger, 2017; Lee & Bond, 2022; Lee & Strauss, 2016;Strauss et al., 2015), with substantial contributions to vegetation dynamics, food webs, and community ecology.
In regions without geographic barriers Masai giraffes can roam over large areas, with mean home range of 114 km 2 for females and 157 km 2 for males (Knüsel et al., 2019), including among-population movements across unprotected human-altered lands in the protected areas of the Tarangire Ecosystem east of the GRE Lee & Bolger, 2017). By contrast Masai giraffe populations in the Serengeti Ecosystem west of the GRE are dispersed over a large single, protected landscape mostly free of human settlements and agriculture, although whether giraffes move between dispersed populations in the Serengeti Ecosystem is unknown.
However, overall wildlife movements within Tanzania have been constricted by rapid expansion of agriculture and human settlements (Caro & Davenport, 2016;Jones et al., 2009;Lamprey, 1964;Riggio et al., 2022;Riggio & Caro, 2017). Although savannah elephants are known to cross the GRE (Douglas-Hamilton, 1973;Lohay et al., 2020;Prins et al., 1994), most other wildlife movements eastwest or around the formidable cliffs of the GRE are rare (Baker et al., 1972;Scoon, 2018). Because the escarpments of the Gregory Rift were well established by 1 mya (Macgregor, 2015) and prior to the emergence of the Masai giraffe as distinct species, the presence of Masai giraffes on the western and eastern sides of the GRE strongly suggests that at some point in their evolutionary history Masai giraffes migrated across or around the escarpments or that they were possibly founded independently from another source population.
A critical question germane to the long-term conservation and management of Masai giraffes is whether the populations located east and west of the GRE are now reproductively isolated and unable to maintain genetic diversity across the populations through dispersal and gene flow. Further, populations within the eastern and western regions are growing more fragmented and may be losing the capacity for genetic exchange .
Population genetic analysis provides a means to ascertain gene flow between populations, and female-vs. male-mediated gene flow can be assessed by comparing mitochondrial DNA (mtDNA) variation vs. nuclear DNA variation because mtDNA is strictly maternally inherited whereas nuclear DNA is inherited from both parents (Allendorf, 2017;Allendorf et al., 2010). An earlier study of the population genetics of major giraffe subspecies (Brown et al., 2007) based on a small fragment of the mtDNA and a small number of microsatellite nuclear markers showed significant differentiation among populations in southern Kenya and northern Tanzania across the Gregory Rift Valley and suggested that the Masai giraffe may constitute more than one species. The objectives of our study were to determine if the steep escarpments impacted genetic differentiation between Masai giraffe populations that occur east and west of the GRE, and to measure genetic exchange among populations, and levels of inbreeding within populations, in each region (Figure 1b). To address these questions, we employed a suite of analytic tools to conduct a population genetic analysis of whole genome sequencing (WGS) data of the mtDNA, and nuclear genomes of population samples obtained from both sides of the GRE in the Serengeti Ecosystem and the Tarangire Ecosystem in northern Tanzania and assessed potential giraffe dispersal routes across the GRE based on maximal slopes.

| Study sites
Our study sites included the Serengeti and Tarangire ecosystems located in northern Tanzania (Figure 1b). Surveys that were con- MRC is an open area supported by the African Wildlife Foundation for wildlife conservation and livestock keeping, and functions as part of a wildlife corridor between the TNP and the LMNP and the Lake Natron area . BWMA, located between the TNP and the LMNP, is a community-based conservation initiative started about 20 years ago by several villages (Lee, 2018). The BWMA is used for promoting eco-tourism and provides habitat for several wildlife species. The BWMA is also part of the corridor connecting the TNP and the MRC and Lake Natron (Kiffner et al., 2020;Lee, 2018). The BMWA giraffe samples for whole genome mtDNA and nuDNA were included with the Tarangire National Park samples because they are geographically adjacent, Masai giraffe freely move between the TNP and BWMA, and no geographic obstacles impede movement. The Serengeti National Park (SGNP) and the Ngorongoro Conservation Area (NCA) form a major part of the Serengeti Ecosystem. While the SGNP is reserved for photo tourism and wildlife management, the NCA allows tourism and pastoralism. Over the past few years, the number of livestock and humans has increased within the NCA (Catherine et al., 2015).

F I G U R E 1
Masai giraffe (Giraffa camelopardalis tippelskirchi) distribution in Tanzania and study areas in the Serengeti and Tarangire ecosystems. (a) Distribution of major populations in Tanzania (shaded) with names of national parks (NP) and game reserves (GR) and ecosystems (boundaries not shown). Masai giraffe populations extend north from the Serengeti ecosystem to the Masai Mara in Kenya and from the Mkomazi GR into the Tsavo NP in Kenya. The location of the Manyara-Natron escarpment and the Eyasi escarpment of the Gregory Rift system bisect Masai giraffe populations in the Serengeti and Tarangire. (b) Study area including the Serengeti National Park (SGNP) and Ngorongoro Conservation Area (NCA) west of the Manyara-Natron and Eyasi escarpments and the Tarangire National Park (TNP), Manyara Ranch Conservancy (MRC), Burunge Wildlife Management Area (BWMA), and Lake Manyara National Park (LMNP) east of the escarpments. The Ngorongoro Highlands together with the Manyara National Park and Eyasi escarpment pose a formidable barrier to terrestrial wildlife movements. Populations census numbers are shown for the Serengeti ecosystem (10,696) and the Tarangire ecosystem (2777)

| Fecal sample collection
We obtained fecal samples from 320 Masai giraffe in six protected areas in Tanzania including the TNP, BWMA,  protected area, we sampled giraffes from several localities typically separated by more than 1 km to reduce the probability of sampling highly related individuals. Once giraffes were sighted, we observed and waited for them to defecate. We photographed each giraffe, recorded its sex, and estimated its age using morphological characteristics after (Strauss et al., 2015). We collected giraffe fecal samples as soon as possible after defecation because giraffe pellets dry quickly. We collected the epithelial cells adhering to the outside layer of pellets (2-4 pellets). We used a razor blade to scrape/peel the thin outer layer from each pellet and placed it into a 50 mL tube.
We added Queen's College buffer (Ahlering et al., 2012) immediately into the tube containing samples.

| Tissue sample collection
We used remote biopsy darts to obtain tissue samples from 100 Animals with similar coat patterns are more likely to be related . To ensure that we have unique individuals, we matched photographs of spot patterns from the biopsy samples using WILDID software to detect replicates (Bolger et al., 2012).

| DNA extraction, PCR amplification and sanger sequencing of mitochondrial DNA fragments
We extracted fecal DNA using the QIAamp PowerFecal DNA kit (QIAGEN) and isolated tissue DNA with the Monarch Nucleic Acid Purification Kits using the manufacturer's protocol, but we increased incubation time to 12 h. to ensure the whole tissue was completely lysed. We extracted DNA from the samples at the Nelson Mandela African Institution of Science and Technology.
To evaluate the relationship between population pairwise F ST and geographic distance, we used two methods: regression of F ST /1−F ST on geographic distance (km) (Rousset, 1997b) and the Isolation by Distance (IBD) Mantel test (Bohonak, 2002;Jombart, 2008), which is based on the correlation between Slatkin's linearized pairwise F ST and geographical distance (Slatkin, 1993).

| Whole genome sequencing
To assess nuclear genetic variation, we analyzed the whole genome short-adapter-reads = 10) and the bases run into the adapter were turned to Ns. The 75 samples sequenced at low coverage yielded ~1.66× average peak depth of mapped reads and the 25 samples sequenced at medium coverage yielded ~24.1× average peak depth of mapped reads. In order to avoid the magnitude difference in coverage in the population analyses, we down-sampled the 25 medium coverage reads by 85% using the random sample process (seed = 11) in seqkit v0.11.0 (Shen et al., 2016). The final average coverage of these 25 down-sampled samples was ~3.09× average peak depth of mapped reads.

| Raw DNA sequence processing and quality control
We evaluated the raw DNA genomic sequences using FastQC v0.11.8 (Andrews, 2010;. The mean quality score for all sample sequences ranged between 34.78 ~ 35.34. FastQC Per Base Sequence Quality showed all bases with the exception of the last base of each read had a quality score higher than 24. Only three samples whose 90th percentile quality score at the last base were above 10, while other samples' were above 18. We kept all bases in the analyses. Since any read whose end overlapped the adaptor had been masked by bcl2fastq, the FastQC Adapter Content report indicated "no adapter found". Fastp v0.20.0 (Chen et al., 2018) was used to check overrepresented sequences and to confirm that it was unnecessary to perform the adapter removal step.

| Reference genome
We used the Masai giraffe genome assembly ASM165123v1 (GCA_001651235.1) that was previously reported (Agaba et al., 2016), and improved by using HiC data to generate chromosomal level assemblies (Dudchenko, 2019) employing the Jucier assembly methods (Dudchenko et al., 2017;Durand et al., 2016). We used the sequences from the fourteen autosomes and sex chromosome from the HiC assembly (ASM165123v1_HiC.fasta.gz) as the reference genome and mapped DNA sequence reads of each individual giraffe to the chromosome assemblies.

| Preparation of alignment files
We aligned the paired-end reads against the reference using the BWA-MEM algorithm of BWA v0.7.17-r1188   For the 25 samples that were down-sampled, the alignment outputs were first filtered of unmapped reads and then sorted by sequence name into bam format with Samtools. Subsequently, we removed duplicates using the Picard MarkDuplicates tool with both "REMOVE_DUPLICATES" and "REMOVE_SEQUENCING_ DUPLICATES" flags set to true. The resulting bam files were then sorted again by coordinates. We generated the final genome alignment and coverage statistics using Samtools stats and BEDTools (Quinlan & Hall, 2010).

| Inbreeding coefficient
We estimated individual inbreeding coefficients F using ngsF (Vieira et al., 2013). The individual inbreeding coefficient here is defined as the proportion of the sites across the genome of that individual, where the observed alleles are identical by descent. The ngsF program requires a binary genome likelihood input file in BEAGLE format. We generated genotype likelihood (GL) files using ANGSD v0.939-10 command from input bam files with "-doGlf 3" as the output option. These bam files were filtered to exclude reads that failed vendor quality checks, that were nonuniquely mapped, whose mate was not mapped, and whose mapping quality was below 30.
The mapping quality in indel regions was adjusted with the flag "-C 50" to adjust mapping quality containing excessive mismatches. All the bases with base quality below 30 were discarded. The aforementioned processes of filtering were applied to the bam files, which were then used as input to calculate GL in the entire study. We es-

| Population structure
We conducted principal component analyses on all samples using PCAngsd (Meisner & Albrechtsen, 2018). This program applies a novel approach of estimating individual allele frequencies to compute a covariance matrix. It relaxes the assumption of a conditional independence between individuals given the population allele frequency. PCAngsd requires a GL input file in a BEAGLE genotype likelihood format. We calculated the GL using the same steps as described in the inbreeding coefficient calculations except replacing the output flag of "-doGlf 3" with "-doGlf2".
The covariance matrix file (.cov file) was converted into eigenvectors and plotted using the R version 4.2 (R Core Team, 2021). We estimated the individual admixture proportions with NGSadmix v32 (Skotte et al., 2013). Two parameters were implemented to determine the convergence: log likelihood difference in 50 iterations (-tolLike50) and tolerance for convergence (-tol). Default values of 0.1 and 1e−5, respectively, were used in this calculation.
The same input GL files for PCAngsd were used in this analysis.
We computed admixture for K from 1 to 5 for five subpopulations where K is the number of ancestry states. The log likelihood of each K was extracted from the output log file and plotted against the K value. From the plot, the value of K was determined as the point at which the slope of line segments shifts downward. The result from NGSadmix (.qopt file) contains the inferred proportions for each individual and was plotted using the R package "xadmix" (Schönmann, 2022).

| Population genetic differentiation
To estimate genetic differentiation between the populations from different regions, we calculated the F ST using ANGSD (Korneliussen et al., 2014) for each population pair. The calculations consist of two steps for each population. First, we estimated the sample allele frequency (SAF) likelihood using ANGSD (-doSaf 1). Then, the results from the first step were used to estimate the folded (with -fold 1 flag) site frequency spectrum (SFS) using realSFS function in the ANGSD package. We calculated the F ST of each population pair using both sample allele frequency likelihoods of the populations and their pairwise SFS as priors in the "fst index" subprogram. The F ST was calculated using the "fst stats" subprogram of realSFS.

| Phylogenetic analyses of mtDNA haplotypes
We used Clustal Omega (Sievers & Higgins, 2021) to generate a nexus alignment of whole genome mtDNAs of representatives of each of the major giraffe subspecies and major mtDNA haplogroups of Masai giraffe. We performed phylogenetic analysis and tree construction using the IQ-tree stochastic algorithm and maximum likelihood method (Trifinopoulos et al., 2016)

| Dispersal route assessment
To ascertain the degree to which the steep escarpments of Gregory Rift may impede giraffe dispersal between the Serengeti and Tarangire ecosystems, we performed a slope assessment of the Manyara-Natron escarpment along its 400+ km length from south central Kenya to north central Tanzania and the Eyasi escarpment that bifurcates from the Manyara-Natron escarpment in the Ngorongoro highlands and terminates ~100 km near the southwest end of Lake Eyasi (Figure 1b). To evaluate the slopes across the escarpments, we generated elevation profiles at 5 km intervals along each escarpment and determined the maximal slope for a perpendicular transect over a 1-5 km distance across the escarpment.
We estimated maximal slopes using Google Earth Engine (Gorelick et al., 2017), which provides accurate estimates of slope comparable to GIS but with more efficient deployment (Safanelli et al., 2020;Yu et al., 2021). For the Manyara-Natron escarpment, the 0 km el-  (Jones et al., 2009) by mapping pathways to minimize slope across the escarpment. If animal tracks were seen, we mapped minimal slope pathways along these tracks.
Genetic distance and geographic distance between populations with restricted dispersal are predicted to be positively correlated giving rise to isolation by distance (Slatkin, 1993;Wright, 1943). To determine if the Masai giraffe populations exhibited significant isolation by distance, we performed pairwise correlation analyses of genetic and geographic distance for three alternative dispersal routes identified as minimal slope passes across the Manyara-Natron escarpment of the Gregory Rift. We mapped these alternative dispersal routes to minimize slopes along their entirety to provide the least resistance to animal movement. Specifically, using satellite view in Google Earth, we drew tracks using the elevation slope function to avoid geographic obstacles (e.g., mountains, hills, ravines, lakes, rivers, and streams). Among the authors (GL, DL, MB, and DC) at least one of us have also driven through all of the areas included in the tracks. We denoted these tracks as least resistance paths. In addition, we assessed the default Euclidean strait line transects between each population.

| Population genetic analysis of mitochondrial genome sequence
To assess mtDNA variation we sequenced a 1140 bp fragment for 320 individuals (Table S1)  We also found that all seven of the Thornicroft's giraffes (G. c. thornicrofti), which mtDNA whole genome sequence data are available, exhibited the WMG1 haplotype ( Figure 3).   (Figure 4b). The Athi River Ranch mtDNA haplotype was previously reported to be closely related to a reticulated giraffe (G. c. reticulata) haplotype, which may have resulted from an introgression event (Petzold & Hassanin, 2020). We also compared the whole genome mtDNA sequence of our 100 samples with whole genome mtDNA sequences obtained from Masai giraffes in the Selous Game Reserve (Coimbra et al., 2021) and Maasai Mara (Agaba et al., 2016) used in the small mtDNA fragment analysis described above. These giraffes exhibit whole genome mtDNA haplotypes that are identical or nearly so to one of the 14 mtDNA haplotype clades described herein. As predicted from their location relative to the Gregory Rift escarpments, the Maasai Mara giraffe exhibited a western Masai giraffe haplotype whereas the Selous Game Reserve giraffes exhibited eastern Masai giraffe haplotypes (Figure 4).

| Nuclear DNA variation
To examine population differentiation of nuDNA variation, we estimated F ST for all pairwise populations in our study (Table 1)  Population structure and admixture analysis showed the presence of two distinct clusters with K = 2 the best fit to the data ( Figure S1). All the SGNP and NCA giraffes were clustered together and all the MRC and TNP giraffes clustered together with at least 90% ancestry (Figure 6a). <10% admixture (fraction of shared membership with the opposite cluster) was seen among these individu-  the EMG populations, three of them showed substantial admixture (25%-40%) from WMG.

| Inbreeding coefficient
We estimated individual inbreeding coefficients, F (Vieira et al., 2013) for each of the 100 WGS nuDNA samples, which are based on a total of 2,054,254 SNPs across the 14 autosomes ( Figure 6b, Table S6).
Individual inbreeding coefficients ranged between 0 and 0.218 and the average was 0.078 across all samples and populations. The average inbreeding coefficient for each population revealed significant differences between eastern and western Masai giraffe populations with WMG populations exhibiting approximately 1.6-fold higher F than EMG populations (Figure 6b). No significant differences in the average F were seen within EMG or WMG populations.

| Dispersal routes and geographic connectivity of the Tarangire and Serengeti ecosystems
Maximum slopes across the Manyara-Natron Escarpment averaged 54.5% and ranged between 31.7% and 79.3% (Table S5, Figure S2) whereas the maximum slopes for the Eyasi Escarpment averaged 60.3% for the first 70 km before declining to <6% at its southwest terminus (Table S5) (Figure 7). We found that the maximum slope of the potential Engaresero WEP traced along the animal tracks across the escarpment was 30.2% (Table S5), which is the lowest maximal slope that we detected along 400 km of the Manyara-Natron Escarpment. By comparison the maximal slope of the Kitete-Selela wildlife corridor located near MRC and LMNP was found to be 50.1% ( Figure S4, Table S5). The only other region identified with a maximum slope <35% corresponded to a location immediately west of Lake Manyara previously identified as an elephant dispersal route (Douglas-Hamilton, 1973;Prins et al., 1994) that we denoted as the Manyara WEP (Figure 7a, Figure S5). The maximal slope of the Manyara WEP is 31.6%. Among these three corridors and passes, we speculated that the maximal slopes of the Manyara and Engaresero WEPs are low enough to potentially support giraffe movement whereas the Kitete-Selela corridor is too  Table S6 for inbreeding coefficients for each individual. **p<0.02, ***p<0.001.

| Landscape genetic connectivity
To evaluate alternative dispersal routes between the four major populations studied (TNP, MRC, NCA, and SGNP), we estimated the correlation between pairwise population F ST estimates (Table 1, Figure S6) of mtDNA and nuDNA genetic variation with pairwise geographic distances (n = 6 pairwise comparisons). We evaluated three potential dispersal routes (Figure 7b) as well as the Euclidean distances between populations using two methods to estimate the relationship of F ST and geographic distance: the Rousset methodregression of F ST /(1−F ST ) on geographic distance (Rousset, 1997a), and the isolation by distance (IBD) Mantel test (Bohonak, 2002). A highly significant correlation between mtDNA F ST and geographic distance was found for the Manyara-Eyasi WEP (R 2 = 0.844**), while significant correlations for nuDNA F ST were found for the Engaresero-Salei WEP (R 2 = 0.880**) and the Manyara-Eyasi WEP F I G U R E 7 Slope and isolation by distance analysis of the Manyara-Natron and Eyasi escarpments of the Gregory Rift system. (a). Satellite map (Google Earth) of northern Tanzania and southern Kenya with the Manyara-Natron and Eyasi escarpments of the Gregory Rift highlighted by black lines. Combined with Ngorongoro highlands, these escarpments pose a formidable barrier to giraffe dispersal between the Serengeti and Tarangire ecosystems. Red lines intersecting the escarpments at 25 km intervals mark the position where maximum slopes were estimated. Maximum slopes were also estimated at 5 km intervals (Table S5 and Figure S5). Yellow lines-a mark the Engaresero-Salei plains potential dispersal route utilizing the Engaresero WEP (red dot) across the Manyara-Natron escarpment near Lake Natron and the Salei Plains (2°38′0.87″S 35°52′46.71″E). Green lines-b mark the Manyara-Highland potential dispersal route utilizing the Manyara WEP (red dot) (3°26′39.87″S 35°48′41.68″E) across the Manyara-Natron escarpment on near the western shore of Lake Manyara. Blue lines-c mark the Manyara-Eyasi potential dispersal route utilizing the Manyara WEP (red dot) across the Manyara-Natron escarpment on near the western shore of Lake Manyara. See Table S5 for maximum slope estimates each of the three dispersal routes.

| DISCUSS ION
The Masai giraffe population has plummeted in the past 30 years as the result of human activities including illegal hunting and land use changes creating fragmented populations with reduced opportunities for dispersal among them (Bolger et al., 2019). In addition to human activities, geographic barriers such as mountains and steep escarpments impede animal movements (Taylor et al., 1972;Wall et al., 2006) and are likely to further constrain dispersal across the Gregory Rift escarpments (GRE). Based on the whole genome mtDNA sequence data, a proxy for female-mediated gene flow, we found that female-mediated gene flow of Masai giraffes has likely not occurred across the GRE in the past ~250,000-300,000 years.
We base this claim upon (1)  The existence of ancient mtDNA haplotype clades within a species is not uncommon. For example, a major mtDNA haplotype of the African savannah elephant is shared with the forest elephant (Ishida et al., 2011(Ishida et al., , 2013 and is more divergent than the Masai giraffe WMG and EMG haplotypes. However, the forest elephant mtDNA haplotype is present in populations east and west of the GRE (Ahlering et al., 2012;Lohay et al., 2020). That the forest elephant mtDNA haplotype is present in major populations east of the GRE whereas the WMG Masai giraffe haplotype is not, is likely due to the different mobility of these two animals in traversing mountainous terrain. The combination of the giraffe's high anterior center of gravity and elevated forelegs and neck (Mitchell, 2021) makes climbing difficult as can be seen in video recordings of giraffes attempting to climb modest inclines (Nat Geo Wild, 2016). Savannah elephants reportedly still traverse the Manyara-Natron escarpment through the Kitete-Selela corridor (Chlebek & Stalter, 2015) and historically crossed this escarpment immediately west of Lake Manyara   (Wright, 1943) and further elaborated by others (Bohonak, 2002;Rousset, 1997a;Slatkin, 1993). The Manyara-Eyasi and Engaresero dispersal routes showed highly significant correlation between nuDNA F ST and geographic distance whereas the Euclidean distance and Manyara-Highlands routes did not. We postulate that the Manyara-Eyasi and Engaresero routes may have served as giraffe dispersal routes in the distant past. However, giraffes are unlikely to have used these routes in recent decades due to anthropogenic changes in the areas above and below the Manyara-Natron escarpments at these locations (Bond et al., 2017;Caro & Davenport, 2016;Jones et al., 2009;Lamprey, 1964;Lee & Bolger, 2017;Prins & de Jong, 2022).

The pairwise nuDNA F ST values between eastern and western
Masai giraffe populations range between 0.0773 and 0.0832, which are comparable to the difference between plains zebra populations in Etosha NP and Luangwa Valley NP separated by 1800 km (Larison et al., 2021) and larger than gray wolf (Canis lupus) populations in British Columbia and Alaska separated by 2000 km of mountainous terrain and between wolf populations in Russia separated by 2100 km (Pacheco et al., 2022).  (Plowright, 1982). The relatively high inbreeding values in Masai giraffes may have resulted from extreme population bottlenecks, population fragmentation, and random drift caused by the rinderpest epidemic. Rinderpest has impacted giraffe populations directly through infection (Plowright, 1982) and indirectly through fire as a result of lower grazing pressure, increasing the fuel load and the subsequent loss of woody browse (Sinclair, 2012). Although rinderpest infections were prevalent east and west of the GRE, the higher wildebeest density in the Serengeti Ecosystem and the greater buffalo density in the Tarangire Ecosystem (Lamprey, 1964) may indicate differential direct and indirect effects of rinderpest on the western and eastern Masai giraffe populations.
Giraffe numbers in the Serengeti and Tarangire Ecosystems did not rebound until the 1970s after widespread cattle vaccinations suppressed the spread of rinderpest to wildlife (Plowright, 1982;Sinclair, 2012). Unfortunately, giraffe census data were not collected before and after the rinderpest epidemic to determine if these two ecosystems were impacted to a different degree, and we have no direct evidence that rinderpest affected the inbreeding levels and population differentiation of Masai giraffes. Alternatively, the mating system and reproductive behavior of giraffes may have resulted in high inbreeding and population differentiation. Currently, there is little information about giraffe mating behavior to determine how philopatry vs. dispersal might influence population genetic structure and relatedness (Bercovitch & Deacon, 2015;.

| Masai giraffe conservation
We have shown compelling evidence that eastern and western Masai giraffes are reproductively isolated and have been so for thousands of years. The apparent reason for their genetic separation is the formidable Gregory Rift Escarpments with maximal slopes that average 50% across a 400+ km extent and only a few passes with slopes between 31%-40%. Therefore, we propose that the Masai giraffe population estimated to be 35,000 should now be considered as two separate evolutionary significant units (ESU) with no more than 20,000 in each. The proposed designation of western Masai giraffe and eastern Masai giraffe as ESUs is based on meeting the specific criteria that the two populations in question are reproductively isolated (Waples, 1995) and that genetically they are "reciprocally monophyletic for mtDNA alleles and show significant divergence of allele frequencies at nuclear loci" (Moritz, 1994).  (Borner, 1985;Lamprey, 1964;Morrison et al., 2016;Mwalyosi, 1991). Based on triannual surveys of individually identified Masai giraffes in the Tarangire Ecosystem, significant dispersal and movement among protected areas in the Tarangire Ecosystem was observed as recently as 2017 Lavista Ferres et al., 2021;Lee & Bolger, 2017). The two largest giraffe populations in the Tarangire Ecosystem are located in the TNP and MRC (Lee & Bolger, 2017) whose boundaries are only 4 km apart. In the past the TNP-MRC-Lake Natron wildlife corridor provided connectivity between MRC and TNP, but this corridor has seen a recent dramatic increase in agriculture and human populations potentially reducing wildlife dispersal (Jones et al., 2009;Kikoti, 2009;Lohay et al., 2022;Msoffe et al., 2011). This corridor is bisected by a major tarmac highway (A104) that wildlife must cross to move between TNP and MRC and much of the expansion of human activities in the Tarangire Ecosystem has occurred along this road. Maintaining the TNP-MRC-Lake Natron corridor to maintain genetic connectivity between EMG populations in the TNP and MRC is extremely important. We recommend that agriculture and contiguous human settlements be restricted in the areas between these populations, and that speed bumps be installed on the A104 highway and wildlife bridges across it be considered.
The small Masai giraffe population in Lake Manyara National  (2) LMNP was originally found by EMG and subsequently received migrants from WMG. Because LMNP giraffes have a higher proportion of EMG nuDNA admixture, the eastern origin appears more likely.
However, the study of the evolution of the Lake Manyara Basin has shown that over the past 10,000 years Lake Manyara was much larger and may have extended all the way to Lake Natron (Bachofer et al., 2014(Bachofer et al., , 2018. This would have imposed a dispersal barrier between giraffes in the narrow strip of land between the GRE and Lake Manyara and giraffes in the Tarangire Ecosystem lying east of the lake until relatively recently (i.e., in the past hundreds to thousands of years). Therefore, we believe it is possible that LMNP was originally founded by western Masai giraffes dispersing down the escarpment, and then later experienced substantial nuDNA and mtDNA introgression from eastern Masai giraffes after Lake Manyara receded.
Northern and southern access around Lake Manyara, however, has been blocked in the past few decades, terminating gene flow between LMNP and other areas of the Tarangire Ecosystem. The population genetic analysis of the LMNP giraffes suggests that this population is currently healthy, but vulnerable to stochastic events such as the emergence of infectious diseases, a major climatic event influencing resource availability/distribution or a major geological event that alters the landscape.

ACK N OWLED G M ENTS
Funding for this project was provided by the Department of Biology,

DATA AVA I L A B I L I T Y S TAT E M E N T
Mitochondrial DNA sequences for 1140 bp can be accessed using GenBank Accession Number: OP442601-OP442932. Whole genome sequence data for the mitochondrial has been submitted to GenBank (GenBank Submissions grp 8715258). Dryad, 10.5061/ dryad.m905qfv4h.

B EN EFIT-S H A R I N G S TATEM ENT
Benefits Generated: A research collaboration was developed with scientists from the Tanzania providing genetic samples, all collaborators are included as co-authors and/or acknowledged for their contributions, and the research addresses a priority concern, in this case the conservation of Masai giraffe in Tanzania and more broadly giraffe East Africa. Our group is committed to international scientific partnerships, as well as institutional capacity building.